<ul data-eligibleForWebStory="true"><li>Multimodal Large Language Models (MLLMs) are being utilized for multimodal graph learning, incorporating structured graph information.</li><li>MLLMs can enhance graph neural networks (GNNs) through multimodal feature fusion and align multimodal attributes for LLM-based graph reasoning.</li><li>There are three paradigms in MMG learning based on MLLM usage: Encoder, Aligner, and Predictor.</li><li>Graph-MLLM is introduced as a benchmark for multimodal graph learning, evaluating the three paradigms across six datasets.</li><li>Jointly considering visual and textual attributes of nodes improves graph learning, even with pre-trained text-to-image alignment models like CLIP as encoders.</li><li>Converting visual attributes into textual descriptions further enhances performance in graph learning compared to using visual inputs directly.</li><li>Fine-tuning MLLMs on specific multimodal graphs can achieve top-tier results, even without explicit graph structure information.</li><li>The presented benchmark aims to provide a fair evaluation framework for MMG learning and encourage further research in the field.</li></ul>

Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning

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